Analysis of adaptive proportional fair scheduling method on BD-GMD scheme for MIMO systems

The Proportional Fair Scheduling (PFS) algorithm is always exploited in multiple input multiple output systems to make a balance between maximising the system throughput and guaranteeing the data rate fairness among different users. Besides the traditional PFS, many modified PFS algorithms have also been proposed to further improve user fairness. Among these, an adaptive PFS algorithm attracts our attention. It defines a new priority number which is multiplying traditional PFS priority number by a factor. And this very factor is the ratio of instantaneous request rate over average request rate. What is different is that while the average transmission rate in traditional PFS has a fixed definition and updating strategy, the average request rate in the adaptive method may not. In this paper, we analyse the adaptive PFS algorithm under different definition and updating strategies on block-diagonal geometric mean decomposition scheme and investigate their performance.

[1]  Wessam Ajib,et al.  An overview of scheduling algorithms in MIMO-based fourth-generation wireless systems , 2005, IEEE Network.

[2]  A. Lee Swindlehurst,et al.  A vector-perturbation technique for near-capacity multiantenna multiuser communication-part I: channel inversion and regularization , 2005, IEEE Transactions on Communications.

[4]  Yi Jiang,et al.  MIMO transceiver design using geometric mean decomposition , 2004, Information Theory Workshop.

[5]  David Stuart Robertson,et al.  Evolution in ecological agent systems , 2011, Int. J. Bio Inspired Comput..

[6]  Marta Kwiatkowska,et al.  A biologically inspired QoS routing algorithm for mobile ad hoc networks , 2010, Int. J. Wirel. Mob. Comput..

[7]  Martin Haardt,et al.  An introduction to the multi-user MIMO downlink , 2004, IEEE Communications Magazine.

[8]  Gang Su,et al.  Adaptive Proportional Fair Scheduling Based on Opportunistic Beamforming for MIMO Systems , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[9]  Ying-Chang Liang,et al.  Block-Diagonal Geometric Mean Decomposition (BD-GMD) for Multiuser MIMO Broadcast Channels , 2006, 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications.

[10]  Shlomo Shamai,et al.  The capacity region of the Gaussian MIMO broadcast channel , 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..

[11]  Gang Su,et al.  A Novel Dynamic Proportional Fair Scheduling Based on Block Diagonal Geometric Mean Decomposition , 2011, J. Networks.

[12]  Caro Lucas,et al.  Imperialist competitive algorithm for minimum bit error rate beamforming , 2009, Int. J. Bio Inspired Comput..

[13]  David Tse,et al.  Opportunistic beamforming using dumb antennas , 2002, IEEE Trans. Inf. Theory.

[14]  Jian Li,et al.  The geometric mean decomposition , 2005 .

[15]  Xiaofeng Wang,et al.  Flexible call admission control for multiclass services in wireless LANs , 2006, Int. J. Wirel. Mob. Comput..

[16]  Sugata Sanyal,et al.  To filter or to authorize , 2008 .

[17]  Shlomo Shamai,et al.  On the achievable throughput of a multiantenna Gaussian broadcast channel , 2003, IEEE Transactions on Information Theory.

[18]  Martin Haardt,et al.  Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels , 2004, IEEE Transactions on Signal Processing.

[19]  Max H. M. Costa,et al.  Writing on dirty paper , 1983, IEEE Trans. Inf. Theory.

[20]  Shensheng Tang,et al.  Modelling and evaluation of the 3G mobile networks with hot-spot WLANs , 2007, Int. J. Wirel. Mob. Comput..

[21]  Shlomo Shamai,et al.  The Capacity Region of the Gaussian Multiple-Input Multiple-Output Broadcast Channel , 2006, IEEE Transactions on Information Theory.